Senior Software Engineer - Planner GPU Compute

Zoox made the world’s first purpose-built commercial robotaxi, combining advanced self-driving hardware and software. In order to ensure safe operation, low latency and consistent resource utilization are critical. The Planner Compute team is responsible for the performance of the largest single software component within the autonomy stack, the motion planner.

The Planner Compute team is looking for an expert in GPU performance. You will instrument, monitor, analyze and optimize GPU-based algorithms that are performance-critical for our solution. The scope for GPU usage ranges from machine learning inference to custom CUDA kernels written specifically for the motion planner.

There are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. A sign-on bonus may be offered as part of the compensation package. The listed range applies only to the base salary. Compensation will vary based on geographic location and level. Leveling, as well as positioning within a level, is determined by a range of factors, including, but not limited to, a candidate's relevant years of experience, domain knowledge, and interview performance. The salary range listed in this posting is representative of the range of levels Zoox is considering for this position.

Zoox also offers a comprehensive package of benefits, including paid time off (e.g. sick leave, vacation, bereavement), unpaid time off, Zoox Stock Appreciation Rights, Amazon RSUs, health insurance, long-term care insurance, long-term and short-term disability insurance, and life insurance.

Responsibilities

Analyze performance metrics to identify GPU hotspots and optimizations; Contribute to the adaptation of our current planner to multiple GPU architectures with different resources; Optimize ML models for latency and GPU memory usage; Support engineers within Planner as a subject matter expert on CUDA & GPU performance

Requirements

BS in computer science or related field and 8+ years of experience.; Strong knowledge of CUDA as applied to recent GPU microarchitectures (e.g., Ampere, Blackwell) and experience debugging/optimizing GPU kernels using tools like Nsight.; Strong knowledge of C++ and experience in large code bases, comfortable in Linux development environments.; Experience in development, debugging, and profiling of complex multiprocess systems (e.g., robotic systems, game engines).

Nice to have

GPU kernel development experience